Evidence map›Paper›PMID 40619444›Full record

ArticleAdvances in simulation (London, England)2025

Navigating complexity: a conceptual framework for simulation interventions.

Anders L Schram, Tine Brink Henriksen, Helle Terkildsen Maindal, Victoria Brazil

Abstract read
In one paragraph

Article in Advances in simulation (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anders L SchramDepartment of Clinical Medicine, MidtSim, Aarhus University, Hedeager 5, Aarhus, 8200, Denmark. anders.schram@rm.dk.
Tine Brink HenriksenDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Helle Terkildsen MaindalDepartment of Public Health, Aarhus University, Aarhus, Denmark.
Victoria BrazilFaculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare systems are inherently complex, shaped by dynamic interactions and interdependencies rather than rigid structures. Simulation-based training interventions must embrace this complexity. Complex Adaptive Systems and Resilient Healthcare provide complementary theoretical frameworks for understanding how healthcare systems can respond to internal and external needs while maintaining adaptability and functionality. Incorporating concepts from Complex Adaptive Systems and Resilient Healthcare into simulation-based interventions increases the likelihood of their success within contemporary healthcare systems. A focus on adaptability, continuous learning, and system-wide resilience is necessary for healthcare improvement, and simulation interventions can help develop and reinforce these capabilities. In this article, we argue that simulation must be reimagined to reflect the realities of complex healthcare systems and propose a conceptual framework to support this shift. MAIN BODY: We propose a three-component conceptual framework for simulation practitioners seeking to design and deliver interventions that embrace complexity: (1) Problem identification, (2) simulation design, and (3) evaluation strategies. The three components function across organizational levels, supporting a dynamic and adaptive approach to addressing healthcare system challenges. By integrating Complex Adaptive Systems and Resilient Healthcare principles, simulation-based interventions can foster a complexity-aware mindset, enabling healthcare professionals and organizations to anticipate, respond to, and recover from challenges more effectively. To illustrate this framework, we introduce three vignettes demonstrating how simulation-based interventions may benefit at different levels within healthcare systems. The vignettes illustrate how challenges at the institutional, departmental, and individual levels can be identified and addressed effectively by simulation-based interventions.

conclusionSimulation interventions can strengthen healthcare systems by supporting organizational learning and embedding principles from complexity science and resilience thinking. This requires reimagining simulation not as isolated training events but as complex interventions that operate across levels and respond to dynamic system needs. By adopting this systems-based approach, simulation practitioners, healthcare leaders, and policymakers can better align simulation with real-world conditions - bridging theory and practice while fostering more adaptive and resilient care.

Identifiers

PMID40619444
PMCPMC12232773

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.